Short-term load forecasting of the National Power Corporation and the MERALCO Power Systems using artificial neural networks - Dellota, Ronoel M. (1996)
The main purpose of short-term load forecasting (STLF) is to predict the hourly loads a few hours to a few weeks in advance. Precise STLF represents great savings for electric utilities because the load forecast is used to control operations decisions such as economic dispatch, unit commitment, scheduling of fuel purchases, security anaylsis and short-term maintenance scheduling. In this regard, an artificial neural network (ANN) based short-term load forecasting is developed and implemented to predict the hourly electric load of the Luzon grid of the National Power Corporation (Napocor) and the Meralco power systems one day to one week beforehand. In addition, a procedure to forecast the daily peak load of the franchise area of Meralco one day in advance is also presented. Two years of historical load and weather data are used for creating the training and testing patterns. The ANNs are then employed to learn the implicit non-linear relationship among the past, current and future weather variables and loads. A multilayer feedforward neural network model is designed and tested in this study and the backpropagation algorithm with adaptive learning rates is utilized to train the ANNs. In this research hourly load forecasts for a one week period were made using the ANN model. For Napocor, an average forecast error of 2.31% was attained. For Meralco, an average forecast error of 2.31 % was reached. For peak load forecasting, an average forecast error of 1.98% was achieved by the ANN model that was used to predict the morning peak load of the Meralco franchise area for a one month period. The results indicate that the ANN-based load forecasting technique gives robust and more accurate forecasts compared with the methodologies that both utility companies are currently using.
Computer print-out.
Thesis (M.S. Electrical Engineering)--University of the Philippines, Diliman.
Keywords: Electric utilities -- Management. -- Philippines; Electric power consumption -- Forecasting; Neural networks (Computer Science).